Primary Partnership: Princeton University Research Computing

Focus: Managing the Research Software Engineering Group

As manager of the group, Ian’s focus is helping his team develop best practices as they help researchers build, develop, debug, and optimize serial/parallel scientific codes.  Ian teaches numerous mini-courses on HPC including “Introduction to Parallel Computing” and “Performance Tuning for Beginners.”

Background:  Bachelor of Mechanical Engineering, University of Delaware, M.S. Mechanical Engineering, Syracuse University and Ph.D. Mechanical Engineering, University of Pennsylvania.

In his research career, Ian developed the first highly-parallel hybrid atomistic-continuum model for liquid-vapor phase change. Previously he has held roles as a Performance Tuning Analyst and Research Computing Software & Programming Analyst.

Ian can be reached at (609) 258-2316 or [email protected].

Primary Partnership: Princeton University Research Computing

Focus: Contributes to the leadership and direction of the Research Software Engineering Group

Joel is a member of the RSE leadership team. In this role, he manages part of the RSE group's project portfolio with the goal of supporting the RSEs in their efforts to develop high-quality research software that helps Princeton researchers advance their scholarly endeavors. His current portfolio ranges across biology, engineering, social science, and the humanities.

Background: Ph.D. in Mechanical Engineering.

Joel's introduction to research software and high-performance computing came as a graduate student working in computational fluid dynamics. Prior to joining the RSE group at Princeton, Joel held a leadership position in computational science as a contractor with the federal government's High Performance Computing Modernization Program.

Joel can be reached at (609) 258-1579 or [email protected].

Primary Partnership: Princeton University Research Computing

Focus: Contributes to the leadership and direction of the Research Software Engineering Group

David is a member of the RSE leadership team. In this role, he manages part of the RSE group's project portfolio with the goal of supporting the RSEs in their efforts to develop high-quality research software that helps Princeton researchers advance their scholarly endeavors. His current portfolio includes hydrology, seismology, high energy physics, plasma physics, astrophysics, and neuroscience.

Background: Ph.D. in Mechanical and Aerospace Engineering.

David's introduction to research software and high-performance computing came as a graduate student working in computational mechanics. Prior to joining the RSE group, David held a dual position in the department of Geosciences and Research Computing at Princeton University doing High-Performance Computing support and software engineering.

David can be reached at (609) 258-7945 or [email protected].

Primary Partnership: Center for Statistics and Machine Learning (CSML)

Focus: Helping faculty and researchers at CSML improve the quality of their existing code and implement code for new projects.

Background:  Bachelor of Engineering degree in Computer Science, and MS in Computer Science from Michigan State University.

Prior to coming to Princeton, Vineet worked at Brooks Instrument where he implemented models developed by research scientists, automated data-collection procedures throughout the research lab, and developed applications for visualization of data collected through several research projects. He has also worked at Bank of America where he assisted with the development of data analysis tools, and at the Center for Language Education & Research at Michigan State University where he developed globally-deployed solutions for language learning, teaching, and testing.

Vineet can be reached at (609) 258-3331 or [email protected].

Primary Partnership: Department of Molecular Biology

Focus: Development of new analytics pipelines, maintenance of existing packages, and visualization of biological data.

Background:  Bachelor of Engineering and Ph.D. in Computer Science.​

Abhishek completed his doctoral work at Old Dominion University and worked at Oak Ridge National Laboratory as post-doctoral research associate before joining the RSE team at Princeton in June 2019. He is working on projects involving development of a standard scalable high-performance metagenome binning pipeline and visualization of polarity in epithelial cell images.  

He can be reached at (609) 258-2059 or [email protected].

Primary Partnership: The Program in Applied and Computational Mathematics

Focus: Software engineering and optimization of ASPIRE, a package for cryo-EM single particle reconstruction.

Background: B.S. in Mathematics from Temple University, Ph.D. in Mathematics from Drexel University.

Prior to joining the RSE group at Princeton, Josh worked as an Assistant Professor of Mathematics at Kutztown University. His doctoral research involved analytical approximations and numerical simulations of solitary wave solutions to a generalized form of the Fermi-Pasta-Ulam-Tsingou lattice, a nonlinear system of coupled oscillators of infinite length.

Josh can be reached at (609) 258-8206 or [email protected]

Primary Partnership: Chemical and Biological Engineering (CBE)

Focus: Helping researchers in the Brangwynne lab improve their codebases and implement robust workflow specification.

Background:  B.S in Computer Science, Chemistry, Mathematics, Biochemistry and Cellular Biology.  Ph.D. in Analytical Chemistry.

Troy joined as an RSE in 2018. Within the Brangwynne lab, he applies rigorous software development practices to develop new analysis pipelines, quantitative fluorescence microscopy, and improve legacy code bases.  Past research areas include 3D bioprinting, single cell mass spectrometry, and human population genomics.

He can be reached at (609) 258-0080 or [email protected].

Primary Partnership: Department of Civil and Environmental Engineering

Focus: Software engineering and machine learning for hydrologic modeling

Background: B.A. in Mathematics from Hamilton College

Amy joined Princeton as a Research Software Engineer in December 2021 to contribute to the HydroGEN hydrologic modeling platform in association with Reed Maxwell’s Lab. Prior to this role, Amy spent over five years working at the intersection of data analysis and public policy, most recently evaluating nutrition, education, and early childhood programs at Mathematica.

She can be reached at [email protected].

Primary Partnership: Operations Research & Financial Engineering (ORFE) 

Focus: Data science and optimization for the ORFEUS project based on operational risk financialization of electricity under stochasticity  

Background: B.A. in Economics and Mathematics, M.S. in Financial Engineering 

Junying (Alice) joined Princeton Research Computing in 2022 after working as a Research Professional at The University of Chicago, where her primary project involved developing statistical models to better estimate market demand. This involved developing and implementing the simulation methods for estimating the individual consumer choice, as well as building packages for deploying optimization models on a high-performance computing cluster. Her present work focuses on working on optimizing pipelines for generating quantitative assessments of the contributions various types of assets can make to a power grid’s ability to satisfy the demand for electricity over a given time frame. 

She can be reached at [email protected]. 

Primary Partnership: Operations Research & Financial Engineering (ORFE)

Focus: Providing software development support for the ORFEUS project based around effectively incorporating renewable sources of energy into modern electricity markets.

Background: BMath in Combinatorics & Optimization, SM in Electrical Engineering and Computer Science

Michal joined Princeton Research Computing in 2021 after five years working as a Research Software Engineer at Oregon Health & Science University, where his primary project involved studying the application of machine learning models to better understand the impacts of mutations commonly implicated in tumorigenesis. This involved implementing novel methods for representing the taxonomies of mutations present in cancer cohorts, as well as developing software for deploying and consolidating thousands of classification models on a high-performance compute cluster. His present work focuses on optimizing pipelines for generating quantitative assessments of the contributions various types of assets can make to a power grid’s ability to satisfy the demand for electricity over a given time frame.

He can be reached at (609) 258-6865 or [email protected].

Primary Partnership: High Meadows Environmental Institute

Focus: Helping faculty and researchers create commercial quality products to provide access to the research technology to water managers and planners so they can directly manipulate state-of-the-art tools to explore scenarios that matter to them.

Background: Bachelor of Science degree in Mathematics and Computer Science from UCLA, and MS in Electrical Engineering and Computer Science from UC Berkley.

Prior to coming to Princeton, Bill worked at Siemens Corporate Research in Princeton in the Software Engineering group doing research in software testing and consulting in all aspects of Software Engineering with the many Siemens divisions all over the world. He transferred to the Siemens Medical group working in data analytics of patient medical information in a large data warehouse and was software architect for several successful products. He migrated to Cerner Corporation when it acquired the Siemens Medical IT division and did design and development of a Cerner product using data from the patient data warehouse. He was a director at medical startup at Geneia that was a spin-off of a large medical insurance company using machine learning and AWS cloud-based technologies.

Bill can be reached at (609) 250-8973 or [email protected]

Primary Partnership: Department of Geosciences

Focus: Performance portability of SpecFEM using Kokkos

Background: Ph.D. in Mechanical Engineering

Rohit joined Princeton as a Research Software Engineer in June 2022 to contribute to seismology codebase (SpecFEM), working alongside Jeroen Tromp's Lab. Prior to joining Princeton, Rohit spent over two years working as a Research Software Engineer at Center for Computation and Visualization at Brown University, where he worked with computational neuroscientists from Carney Institute of Brain Sciences to develop deep learning pipelines utilized in automated animal behavioral analysis.

He can be reached at [email protected]

Primary Partnership: Program in Applied and Computational Mathematics

Focus: Development of ASPIRE, a software package for reconstruction of cryo-electron microscopy images

Background: B.S. Applied Mathematics

Chris studied math at the University of Rochester, where he was involved with research in linguistics and, later, molecular simulation. He then worked as a research assistant at Columbia University Medical Center, where he developed software tools for preprocessing and analyzing fMRI and structural brain scans. Chris joined the RSE group at Princeton in August 2021.

He can be reached at 609-258-8206 or [email protected]

Primary Partnership: Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP)

Focus:  Developing foundational tools in support of a high-data volume analysis system in Python for the future runs of the LHC.

Background:  B.S. in Physics, Ph.D. in High Energy Physics from the University of Texas at Austin.

Prior to coming to Princeton, Henry worked on computational cosmic-ray tomography for archeological applications at the University of Texas. As a postdoc at the University of Cincinnati, he worked on high performance GPU model fitting, real-time trigger improvements, and developer training for the LHCb experiment. Now he specializes in the interface between high-performance compiled codes and interactive computation in Python, in software distribution, and in interface design. He is an admin of Scikit-HEP, and has a blog at iscinumpy.gitlab.io.

He can be reached at (609) 258-8141 or [email protected].

Primary Partnership: Data-Driven Social Science (DDSS)

Focus: Data Engineering, Machine Learning Engineering

Background: B.S. in Mathematics and Economics, M.S. in Mathematics, Ph.D. in Finance from the University of Iowa

Colin joined the RSE group at Princeton in May 2021 in affiliation with the Initiative for Data-Driven Social Science. His work focuses on creating open-source statistical software and building systems to manage and facilitate research on large-scale social science databases. In his past research, he has developed methods to forecast high-frequency trade activity and predict mutual fund returns using machine learning methods. Prior to Princeton, Colin held roles as a quantitative researcher at Jacobs Levy Equity Management and as the lead data scientist at Nova Credit Inc.

He can be reached at (609) 258-8980 or [email protected].

Primary Partnership: Princeton Neuroscience Institute (PNI)

Focus: Helping PNI improve the performance and quality of their computational and experimental neuroscience codes.

Background: BS/MS from Drexel University in Computer Science and PhD from Georgia Institute of Technology in Mechanical Engineering.

As a former member of the MiNED research group at Georgia Tech, David is adept at applying machine learning in the field of materials and microstructure informatics, including generative modeling of material microstructure from limited information, image segmentation, and statistical descriptions of material structure. Additional past research areas of interest included networking, security, and operating systems.

He can be reached at (609) 258-2985 or [email protected].

Primary Partnership: Program of Applied and Computational Mathematics (PACM)

Focus:  Software engineering and optimization of ASPIRE, a package for cryo-EM single particle reconstruction.

Background:  B.S. Mathematics

Garrett studied experimental mathematics at Temple University where he focused on novel GPU computations, particularly eigensystems of certain random graph families. Garrett then worked in industry developing peta-scale time series models including production distributed systems and algorithms for quantitative finance in HTC and high frequency streaming domains. Over the years he has worked in HPC roles supporting the Princeton scientific community at GFDL and PPPL. At GFDL he authored their flagship GPU Radiative Transfer Code, GRTCODE. Similarly he developed cuOrbit a CUDA implementation of PPPL's toroidally confined plasma guiding center simulation.

He can be reached at [email protected].